The present embodiments relate to a method for reconstructing image data during CT imaging. Furthermore, the embodiments relate to an image data determination facility. Moreover, the embodiments also relate to a computed tomography (CT) system.
With the help of modern imaging methods, two- or three-dimensional image data is frequently generated that may be used to visualize a mapped examination object and furthermore also for further applications.
The imaging methods are frequently based on the capture of x-ray radiation, wherein what is known as projection measured data is generated. Projection measured data may be acquired for instance with the aid of a CT system. With CT systems, an assembly disposed on a gantry (i.e., an x-ray source and an x-ray detector arranged opposite thereto) may circulate around a measuring space, in that the examination object (that, without loss of generality, is referred to below as the patient) is located. The center of rotation (also referred as ‘isocenter’) coincides here with what is known as a system axis z. In one or a number of circulations, the patient is irradiated with x-ray radiation from the x-ray source, wherein projection measured data or x-ray projection data is captured with the aid of the opposing x-ray detector.
The projection measured data generated, also abbreviated to projection data, is in particular dependent on the model design of the x-ray detector. X-ray detectors may have a plurality of detection units that are in most cases arranged in the form of a regular pixel array. The detection units each generate a detection signal for x-ray radiation striking the detection units, said detection signal being analyzed at specific time instants in respect of intensity and spectral distribution of the x-ray radiation, in order to obtain information on the examination object and to generate projection measured data.
With a series of applications of CT systems, a number of data records or projection data records of independent measurements that relate to the same object are captured. By way of example, the different data records may relate to the same object at different time instants. The different data records may also include image recordings of the object with different recording parameters, such as different spectral parts for example. Such data records are recorded for instance while using recordings having a number of energy thresholds, known as multi-energy scans. With multi-energy scans, data of a quanta-counting detector having one or a number of energy thresholds is captured, wherein different data records are assigned to the respective energy ranges that are separated from the energy thresholds.
With the aforementioned quanta-counting or photon-counting x-ray detectors, the detection signal for x-ray radiation is analyzed in respect of intensity and spectral distribution of the x-ray radiation in the form of count rates. The count rates are made available as output data of what is known as a detector channel, which is assigned to a detection unit in each case. With quanta- or photon-counting detectors having a number of energy thresholds, each detector in most cases generates a set of count rates per projection on the basis of the respective detection signal of the detection unit. With the aid of the set of count rates, data records may be generated for a number of different energy threshold values that are in particular checked at the same time.
The individual different data records have a poorer quanta statistic, i.e. an increased statistical noise, than if the data records were available as a sum. This is then particularly the case if in the case of two energy thresholds, these energy thresholds are arranged closely around the energy value of the K-edge of a material that is used to map this material selectively. A similar problem occurs if, instead of spectrally separated individual images, base material images are to be reconstructed.
The problem overall then is that the statistical quality of the individual data records is significantly poorer than that of the overall data record that results in individual images with artifacts due to noise effects.
One possibility of improving the image quality of the individual images is in using all data records together during the reconstruction of image data, but on this basis, to reconstruct a spectral image or a base material image, wherein the time instant is defined by the system matrix, i.e. by the modeling of the measurement process of the selected spectral component or of the material component. Such an approach is described for instance in W. Huh and J A. Fessler, “Iterative image reconstruction for dual-energy x-ray CT using regularized material sinogram estimates”, IEEE (2011), 1512-1515. Attempts are made here to use the statistics generically, i.e. by statistical weighting of the input data. In respect of a precise reproduction of the structures to be mapped, such an approach is ineffective since the approach entails a strong smoothing, i.e. reduction in the spatial resolution. In other words, the morphological information gets lost during the reconstruction of the individual images. Moreover, due to the complexity of the system matrix and its iterative application, such methods require a very high computing outlay.
With another conventional method, a data record with good statistics is used in a spatial frequency-selective manner as a priori information and a spectral or material component is thus optimized. Although a reconstruction method based on this approach is significantly faster than with the approach described above, this method is similarly disadvantageous in that the method fails to obtain morphological information when the individual images are being reconstructed. The morphological information is only retained here for instance with low spatial frequencies, in other words larger object structures, and gets lost with high spatial frequencies. With both conventional approaches, filtering takes place in the reconstruction act without taking into account the structural information.